% 第一章 注释

% 工业智能化
@Article{sci4030026,
AUTHOR = {Kagermann, Henning and Wahlster, Wolfgang},
TITLE = {Ten Years of Industrie 4.0},
JOURNAL = {Sci},
VOLUME = {4},
YEAR = {2022},
NUMBER = {3},
ARTICLE-NUMBER = {26},
URL = {https://www.mdpi.com/2413-4155/4/3/26},
ISSN = {2413-4155},
ABSTRACT = {A decade after its introduction, Industrie 4.0 has been established globally as the dominant paradigm for the digital transformation of the manufacturing industry. Amalgamating research-based results and practical experience from the German industry, this contribution reviews the progress made in implementing Industrie 4.0 and identifies future fields of action from a technological and application-oriented perspective. Putting the human in the center, Industrie 4.0 is the basis for data-based value creation, innovative business models, and agile forms of organization. Today, in the German manufacturing industry, the Internet of Things and cyber–physical production systems are a reality in newly built factories, and the connectivity of machinery has been significantly increased in existing factories. Now, the trends of industrial AI, edge computing up to the edge cloud, 5G in the factory, team robotics, autonomous intralogistics systems, and trustworthy data infrastructures must be leveraged to strengthen resilience, sovereignty, semantic interoperability, and sustainability. This enables the creation of digital innovation ecosystems that ensure long-term adaptability in a volatile economic and geopolitical environment. In sum, this review represents a comprehensive assessment of the status quo and identifies what is needed in the future to reap the rewards of the groundwork done in the first ten years of Industrie 4.0.},
DOI = {10.3390/sci4030026}
}

% PHM
@article{SUN2023107634,
title = {Preventive maintenance optimization for key components of subway train bogie with consideration of failure risk},
journal = {Engineering Failure Analysis},
volume = {154},
pages = {107634},
year = {2023},
issn = {1350-6307},
doi = {https://doi.org/10.1016/j.engfailanal.2023.107634},
url = {https://www.sciencedirect.com/science/article/pii/S1350630723005885},
author = {Haimeng Sun and Deqiang He and Jiecheng Zhong and Zhenzhen Jin and Zexian Wei and Zhenpeng Lao and Sheng Shan},
keywords = {Urban rail transit train, Preventive maintenance, Failure risk, Improved beluga whale optimization algorithm, Lévy flight},
abstract = {With the acceleration of urbanization, the number of urban rail transit trains has increased dramatically, and the economy and safety of subway trains have become important standards. As an essential subsystem of the subway train, the bogie has a long maintenance process and serious failure consequences. So, it is essential to formulate a reasonable maintenance strategy to ensure its operation. To solve the above problems, a preventive maintenance (PM) strategy for key components of train bogie is established with consideration of failure risk in this paper. Firstly, the failure risk factors of the bogie components are scored and weighted, and the failure risk cost model and PM cost model are established. Next, a Weibull distribution parameter estimation method for bogie components based on the improved beluga whale optimization algorithm (IBWO) is proposed, providing a theoretical basis for PM decision-making optimization. Then, the Lévy flight is introduced into particle swarm optimization to enhance the optimization performance of the model and obtain the optimal solution. Finally, the bogie key components of Nanning Metro line 1 are selected as a case study. The results show that the IBWO algorithm has strong applicability and feasibility, and can accurately calculate the Weibull parameter values of the bogie key components. Compared with the PM plan without considering the failure risk, the proposed method is more economical and safer, which can provide necessary theoretical support for the maintenance decision optimization of urban rail transit train components.}
}

% RUL预测
@article{Li2023ANN,
  title={A new nonparametric degradation modeling method for truncated degradation signals by axis rotation},
  author={Naipeng Li and Yaguo Lei and Xiang Li and Xiaofei Liu and Bin Yang},
  journal={Mechanical Systems and Signal Processing},
  year={2023},
  url={https://api.semanticscholar.org/CorpusID:257117622}
}

@article{CHENG2021101247,
title = {A convolutional neural network based degradation indicator construction and health prognosis using bidirectional long short-term memory network for rolling bearings},
journal = {Advanced Engineering Informatics},
volume = {48},
pages = {101247},
year = {2021},
issn = {1474-0346},
doi = {https://doi.org/10.1016/j.aei.2021.101247},
url = {https://www.sciencedirect.com/science/article/pii/S1474034621000021},
author = {Yiwei Cheng and Kui Hu and Jun Wu and Haiping Zhu and Xinyu Shao},
keywords = {Rolling bearing, Degradation indicator construction, Health prognosis, Convolutional neural network, Bidirectional long short-term memory network},
abstract = {Health prognosis of rolling bearing is of great significance to improve its safety and reliability. This paper presents a novel health prognosis method for the rolling bearing based on convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM) model. First, a new nonlinear degradation indicator (DI) is designed which can be utilized as training label. Then, through learning and capturing the mapping relationship between raw vibration signals and DI of the rolling bearing, a CNN model is introduced to estimate the DI value of the rolling bearing. And, BiLSTM models are set up to carry out health prognosis using the estimated DI, including future DI and remaining useful life prediction. An experiment verification is implemented to validate the effectiveness of the proposed method. Results show the excellent ability of future DI prediction, and demonstrate the superiority of the proposed method in the field of remaining useful life prediction compared with other existing deep learning models.}
}

% RUL定义
@article{SI20111,
title = {Remaining useful life estimation – A review on the statistical data driven approaches},
journal = {European Journal of Operational Research},
volume = {213},
number = {1},
pages = {1-14},
year = {2011},
issn = {0377-2217},
doi = {https://doi.org/10.1016/j.ejor.2010.11.018},
url = {https://www.sciencedirect.com/science/article/pii/S0377221710007903},
author = {Xiao-Sheng Si and Wenbin Wang and Chang-Hua Hu and Dong-Hua Zhou},
keywords = {Maintenance, Remaining useful life, Brown motion, Stochastic filtering, Proportional hazards model, Markov},
abstract = {Remaining useful life (RUL) is the useful life left on an asset at a particular time of operation. Its estimation is central to condition based maintenance and prognostics and health management. RUL is typically random and unknown, and as such it must be estimated from available sources of information such as the information obtained in condition and health monitoring. The research on how to best estimate the RUL has gained popularity recently due to the rapid advances in condition and health monitoring techniques. However, due to its complicated relationship with observable health information, there is no such best approach which can be used universally to achieve the best estimate. As such this paper reviews the recent modeling developments for estimating the RUL. The review is centred on statistical data driven approaches which rely only on available past observed data and statistical models. The approaches are classified into two broad types of models, that is, models that rely on directly observed state information of the asset, and those do not. We systematically review the models and approaches reported in the literature and finally highlight future research challenges.}
}

% 复杂退化过程设备
@article{yang2016bayes,
    author = {杨浩天 and 汪立新 and 田颖 and 谭纪文},
    title = {惯性器件剩余寿命预测非线性退化过程建模的贝叶斯方法},
    journal = {电光与控制},
    year = {2016},
    volume = {23},
    number = {12},
    pages = {90},
    langid = {chinese},
    keywords = {中文标题下的文献相关信息},
    note = {对应英文信息：YANG Hao - tian, WANG Li - xin, TIAN Ying, TAN Ji - wen. Bayes Approach for Nonlinear Degradation Process Modeling of Inertial Device RUL Prediction[J]. Electronics Optics \& Control, 2016, 23(12): 90.}
}

% RUL方法可以分为三类
@inproceedings{Mrugalska2018RemainingUL,
  title={Remaining Useful Life as Prognostic Approach: A Review},
  author={Beata Mrugalska},
  booktitle={International Conference on Human Systems Engineering and Design},
  year={2018},
  url={https://api.semanticscholar.org/CorpusID:69487067}
}

@article{liu2025comprehensive,
    author = {Liu, Yitong and Wen, Jiarui and Liu, Xintian and Yang, Shuqun and Wang, Guoqiang},
    title = {A Comprehensive Overview of Remaining Useful Life Prediction: From Traditional Literature Review to Scientometric Analysis},
    journal = {SSRN Electronic Journal},
    year = {2025}, 
    url = {https://ssrn.com/abstract=5152989},
    doi = {10.2139/ssrn.5152989}
}

@ARTICLE{10311537,
  author={Zhang, Yangyang and Fang, Liqing and Qi, Ziyuan and Deng, Huiyong},
  journal={IEEE Sensors Journal}, 
  title={A Review of Remaining Useful Life Prediction Approaches for Mechanical Equipment}, 
  year={2023},
  volume={23},
  number={24},
  pages={29991-30006},
  keywords={Prognostics and health management;Maintenance engineering;Monitoring;Artificial intelligence;Predictive models;Prediction algorithms;Biological system modeling;Artificial intelligence;Digital twins;Lifetime estimation;Artificial intelligence (AI);digital twin;hybrid prognostic approaches;mechanical equipment;physics model;prognostics and health management (PHM);remaining useful life (RUL);statistical model},
  doi={10.1109/JSEN.2023.3326487}}

% 基于模型的方法 - 介绍
@article{LEE2014314,
title = {Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications},
journal = {Mechanical Systems and Signal Processing},
volume = {42},
number = {1},
pages = {314-334},
year = {2014},
issn = {0888-3270},
doi = {https://doi.org/10.1016/j.ymssp.2013.06.004},
url = {https://www.sciencedirect.com/science/article/pii/S0888327013002860},
author = {Jay Lee and Fangji Wu and Wenyu Zhao and Masoud Ghaffari and Linxia Liao and David Siegel},
keywords = {Diagnostics, Prognostics and health management (PHM), Condition based maintenance, Reliability centered maintenance},
abstract = {Much research has been conducted in prognostics and health management (PHM), an emerging field in mechanical engineering that is gaining interest from both academia and industry. Most of these efforts have been in the area of machinery PHM, resulting in the development of many algorithms for this particular application. The majority of these algorithms concentrate on applications involving common rotary machinery components, such as bearings and gears. Knowledge of this prior work is a necessity for any future research efforts to be conducted; however, there has not been a comprehensive overview that details previous and on-going efforts in PHM. In addition, a systematic method for developing and deploying a PHM system has yet to be established. Such a method would enable rapid customization and integration of PHM systems for diverse applications. To address these gaps, this paper provides a comprehensive review of the PHM field, followed by an introduction of a systematic PHM design methodology, 5S methodology, for converting data to prognostics information. This methodology includes procedures for identifying critical components, as well as tools for selecting the most appropriate algorithms for specific applications. Visualization tools are presented for displaying prognostics information in an appropriate fashion for quick and accurate decision making. Industrial case studies are included in this paper to show how this methodology can help in the design of an effective PHM system.}
}

@article{cubillo2016review,
  author = {Cubillo, A and Perinpanayagam, S and Esperon-Miguez, M},
  title = {A review of physics-based models in prognostics: application to gears and bearings of rotating machinery},
  journal = {Advances in Mechanical Engineering},
  volume = {8},
  number = {8},
  pages = {1--21},
  year = {2016}
}

% 基于模型的方法 - 研究工作
@article{LI1999103,
title = {Adaptive prognostics for rolling element bearing condition},
journal = {Mechanical Systems and Signal Processing},
volume = {13},
number = {1},
pages = {103-113},
year = {1999},
issn = {0888-3270},
doi = {https://doi.org/10.1006/mssp.1998.0183},
url = {https://www.sciencedirect.com/science/article/pii/S0888327098901832},
author = {Y. Li and S. Billington and C. Zhang and T. Kurfess and S. Danyluk and S. Liang},
abstract = {Rolling element bearing failure is one of the foremost causes of breakdown in rotating machinery. This paper proposes a remaining life adaptation methodology based on mechanistic modeling and parameter tuning. Vibration measurement is used to estimate defect severity by monitoring the signals generated from rotating bearings. Through a defect propagation model and defect diagnostic model, an adaptive algorithm is developed to fine tune the parameters involved in the propagation model by comparing predicted and measured defect sizes. In this manner, the instantaneous rate of defect propagation can be captured despite defect growth behavior variation. Therefore, a precise estimation of the remaining life can be determined. Simulations and experimental results are presented to illustrate the implementation principles and to verify the applicability of the adaptive prognostic methodology.}
}

@article{QIAN2021103319,
title = {Hybrid optimization strategy for lithium-ion battery's State of Charge/Health using joint of dual Kalman filter and Modified Sine-cosine Algorithm},
journal = {Journal of Energy Storage},
volume = {44},
pages = {103319},
year = {2021},
issn = {2352-152X},
doi = {https://doi.org/10.1016/j.est.2021.103319},
url = {https://www.sciencedirect.com/science/article/pii/S2352152X21010112},
author = {KF Qian and XT Liu},
keywords = {SOC/SOH estimation, Dual extend Kalman filter, SCA, Parameter estimation online},
abstract = {In order to improve the accuracy of State of Charge / Health (SOC/SOH) estimation, Modified-Sine-cosine Algorithm-based dual Extend Kalman filter (MSCA-DEKF) is proposed. Second-order RC model is applied, the model parameters are obtained by Pulse discharge test and Open circuit voltage test (OCV). DEKF is divided into state filter and parameter filter. Nonlinear decrement of transformation parameter r1 in Sine-cosine Algorithm (SCA) is proposed. Modified SCA (MSCA) is applied to optimizing the covariance noise matrix in the state filter. Parameter filter is applied to estimating the Ohm internal resistance and capacity online, Meanwhile, the time scale of parameters’ online update is adjusted to 60 time steps for reducing computing costs. SOH is also obtained by Ohm internal resistance and capacity. Simulation results show that the proposed method improves the accuracy of SOC estimation, and the initial errors of SOC and Ro can be corrected in the first parameter estimation. Ro-based SOH has a better robustness and precision than capacity-based SOH.}
}

@article{2002Novel,
  title={Novel approach to nonlinear/non-Gaussian Bayesian state estimation},
  author={ Gordon, N. J.  and  Salmond, D. J.  and  Smith, A. F. M. },
  journal={IEE Proceedings F - Radar and Signal Processing},
  volume={140},
  number={2},
  pages={107-113},
  year={2002},
}

% 基于模型的方法 - 缺点
@INPROCEEDINGS{9187043,
  author={Falcon, Alex and D'Agostino, Giovanni and Serra, Giuseppe and Brajnik, Giorgio and Tasso, Carlo},
  booktitle={2020 IEEE International Conference on Prognostics and Health Management (ICPHM)}, 
  title={A Neural Turing Machine-based approach to Remaining Useful Life Estimation}, 
  year={2020},
  volume={},
  number={},
  pages={1-8},
  keywords={Computer architecture;Logic gates;Estimation;Time series analysis;Feature extraction;Prognostics and health management;Machine learning},
  doi={10.1109/ICPHM49022.2020.9187043}}

@article{ZIO201049,
title = {A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system},
journal = {Reliability Engineering \& System Safety},
volume = {95},
number = {1},
pages = {49-57},
year = {2010},
issn = {0951-8320},
doi = {https://doi.org/10.1016/j.ress.2009.08.001},
url = {https://www.sciencedirect.com/science/article/pii/S0951832009002051},
author = {Enrico Zio and Francesco {Di Maio}},
keywords = {Prognostics, Residual useful life, Recovery time, Emergency accident management, Operator support system, Nuclear power plant, Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS), Pointwise fuzzy similarity analysis},
abstract = {This paper presents a similarity-based approach for prognostics of the Remaining Useful Life (RUL) of a system, i.e. the lifetime remaining between the present and the instance when the system can no longer perform its function. Data from failure dynamic scenarios of the system are used to create a library of reference trajectory patterns to failure. Given a failure scenario developing in the system, the remaining time before failure is predicted by comparing by fuzzy similarity analysis its evolution data to the reference trajectory patterns and aggregating their times to failure in a weighted sum which accounts for their similarity to the developing pattern. The prediction on the failure time is dynamically updated as time goes by and measurements of signals representative of the system state are collected. The approach allows for the on-line estimation of the RUL. For illustration, a case study is considered regarding the estimation of RUL in failure scenarios of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS).}
}

@article{Liewald2022Perspectives,
  author = {Liewald, Mathias and Bergs, Thomas and Groche, Peter and Behrens, Bernd-Arno and Briesenick, David and Müller, Martina and Niemietz, Philipp and Kubik, Christian and Müller, Felix},
  title = {Perspectives on data-driven models and its potentials in metal forming and blanking technologies},
  journal = {Production Engineering Research and Development},
  volume = {16},
  pages = {607--625},
  year = {2022},
  doi = {10.1007/s11740-022-01115-0},
  url = {https://doi.org/10.1007/s11740-022-01115-0}
}

% 基于统计模型的方法
@ARTICLE{1951JAM....18..293W,
       author = {{Weibull}, Waloddi},
        title = "{A Statistical Distribution Function Of Wide Applicability}",
      journal = {Journal of Applied Mechanics},
         year = 1951,
        month = jan,
       volume = {18},
        pages = {293-297},
          doi = {10.1115/1.4010337},
       adsurl = {https://ui.adsabs.harvard.edu/abs/1951JAM....18..293W},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{lawless2004covariates,
  author = {Lawless, Jerry and Crowder, Martin},
  title = {Covariates and Random Effects in a Gamma Process Model with Application to Degradation and Failure},
  journal = {Lifetime Data Analysis},
  volume = {10},
  pages = {213--227},
  year = {2004},
  doi = {10.1023/B:LIDA.0000036389.14073.dd},
  url = {https://doi.org/10.1023/B:LIDA.0000036389.14073.dd}
}

@article{CAI2021106983,
title = {Remaining useful life re-prediction methodology based on Wiener process: Subsea Christmas tree system as a case study},
journal = {Computers \& Industrial Engineering},
volume = {151},
pages = {106983},
year = {2021},
issn = {0360-8352},
doi = {https://doi.org/10.1016/j.cie.2020.106983},
url = {https://www.sciencedirect.com/science/article/pii/S0360835220306537},
author = {Baoping Cai and Hongyan Fan and Xiaoyan Shao and Yonghong Liu and Guijie Liu and Zengkai Liu and Renjie Ji},
keywords = {Remaining useful life, Wiener process, Dynamic Bayesian networks, Expectation Maximization algorithm, Subsea Christmas tree system},
abstract = {With the continuous improvement of the complexity and comprehensive level of the system, its reliability becomes more and more important. The remaining useful life (RUL) estimation method using the degradation model with random effect to describe the degradation process of the system has been widely used such as Wiener process. However, the conventional Wiener-process-based degradation model only considers the current monitoring data but not the historical degradation data, which leads to the inaccuracy of RUL prediction. Furthermore, in engineering, there will always be data missing caused by sensor networks, long life cycle properties of system and so on, leading to unsatisfactory results. This paper contributed a RUL re-prediction method based on Wiener process combining the current monitoring status and historical degradation data of the system. In the initial prediction process, the Wiener process is used to describe the degradation process of the system, the drift coefficient and diffusion coefficient are estimated by Expectation Maximization algorithm (EM algorithm), and the dynamic Bayesian networks (DBNs) model for system performance degradation is established to solve the uncertainty caused by missing data. In the re-prediction process, n groups of performance degradation monitoring data and historical predicted data are combined to calculate the basic degradation in each stage of Wiener process, and the DBNs are used for modeling. The RUL value is obtained by the time difference between the detection point and the predicted fault point, it is determined by the failure threshold finally. A case of subsea Christmas tree system is adopted to demonstrate the proposed approach.}
}

@INPROCEEDINGS{9101682,
  author={Yujing, Wang and Shida, Wang and Shouqiang, Kang and Jinbao, Xie},
  booktitle={2019 14th IEEE International Conference on Electronic Measurement \& Instruments (ICEMI)}, 
  title={Health index construction and remaining useful life prediction of rolling bearings}, 
  year={2019},
  volume={},
  number={},
  pages={1241-1247},
  keywords={Feature extraction;Indexes;Rolling bearings;Time-frequency analysis;Predictive models;Correlation;AdaBoost;relevance vector machine;variational mode decomposition;rolling bearing;remaining useful life prediction},
  doi={10.1109/ICEMI46757.2019.9101682}}


@article{Yuan2024Progress,
  author = {Yuan, Jun and Qin, Zhili and Huang, Haikun and Gan, Xingdong and Wang, Ziwei and Yang, Yichen and Liu, Shujiang and Wen, An and Bi, Chuang and Li, Baihai and Sun, Chenghua},
  title = {Progress in the prognosis of battery degradation and estimation of battery states},
  journal = {Science China Materials},
  volume = {67},
  pages = {1014--1041},
  year = {2024},
  doi = {10.1007/s40843-023-2665-8},
  url = {https://doi.org/10.1007/s40843-023-2665-8}
}

@article{Wang01072007,
author = {W Wang and},
title = {A prognosis model for wear prediction based on oil-based monitoring},
journal = {Journal of the Operational Research Society},
volume = {58},
number = {7},
pages = {887--893},
year = {2007},
publisher = {Taylor \& Francis},
doi = {10.1057/palgrave.jors.2602185},
URL = {https://doi.org/10.1057/palgrave.jors.2602185},
eprint = {https://doi.org/10.1057/palgrave.jors.2602185}
}

@ARTICLE{10440207,
  author={Ma, Ping and Li, Guangfu and Zhang, Hongli and Wang, Cong and Li, Xinkai},
  journal={IEEE Transactions on Instrumentation and Measurement}, 
  title={Prediction of Remaining Useful Life of Rolling Bearings Based on Multiscale Efficient Channel Attention CNN and Bidirectional GRU}, 
  year={2024},
  volume={73},
  number={},
  pages={1-13},
  keywords={Feature extraction;Convolution;Degradation;Rolling bearings;Predictive models;Vibrations;Time-domain analysis;Bidirectional gated recurrent unit (BIGRU);Gram angle field;multiscale efficient channel attention convolutional neural network (MSECNN);remaining useful life (RUL) prediction;rolling bearing},
  doi={10.1109/TIM.2023.3347787}}

@article{Deng2024Prediction,
  author = {Deng, Sizhe and Zhou, Jian},
  title = {Prediction of Remaining Useful Life of Aero-engines Based on CNN-LSTM-Attention},
  journal = {International Journal of Computational Intelligence Systems},
  volume = {17},
  number = {232},
  year = {2024},
  doi = {10.1007/s44196-024-00639-w},
  url = {https://doi.org/10.1007/s44196-024-00639-w}
}

@ARTICLE{10654267,
  author={Dhananjay Rao, K. and Ramakrishna, A. and Ramesh, M. and Koushik, Pallanti and Dawn, Subhojit and Pavani, P. and Selim Ustun, Taha and Cali, Umit},
  journal={IEEE Access}, 
  title={A Hyperparameter-Tuned LSTM Technique-Based Battery Remaining Useful Life Estimation Considering Incremental Capacity Curves}, 
  year={2024},
  volume={12},
  number={},
  pages={127259-127271},
  keywords={Batteries;Accuracy;Long short term memory;Degradation;Integrated circuit modeling;Predictive models;Estimation;Capacity planning;Hyperparameter optimization;Incremental capacity curves;remaining useful life;battery degradation;hyperparameter tuned LSTM},
  doi={10.1109/ACCESS.2024.3450871}}

@article{PATIL2015285,
title = {A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation},
journal = {Applied Energy},
volume = {159},
pages = {285-297},
year = {2015},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2015.08.119},
url = {https://www.sciencedirect.com/science/article/pii/S0306261915010557},
author = {Meru A. Patil and Piyush Tagade and Krishnan S. Hariharan and Subramanya M. Kolake and Taewon Song and Taejung Yeo and Seokgwang Doo},
keywords = {Remaining Useful Life, Classification, Regression, Support Vector Machine, Battery life models},
abstract = {Real-time prediction of remaining useful life (RUL) is an essential feature of a robust battery management system (BMS). In this work, a novel method for real-time RUL estimation of Li ion batteries is proposed that integrates classification and regression attributes of Support Vector (SV) based machine learning technique. Cycling data of Li-ion batteries under different operating conditions are analyzed, and the critical features are extracted from the voltage and temperature profiles. The classification and regression models for RUL are built based on the critical features using Support Vector Machine (SVM). The classification model provides a gross estimation, and the Support Vector Regression (SVR) is used to predict the accurate RUL if the battery is close to the end of life (EOL). By the critical feature extraction and the multistage approach, accurate RUL prediction of multiple batteries is accomplished simultaneously, making the proposed method generic in nature. In addition to accuracy, the multistage approach results in faster computations, and hence a trained model can potentially be used for real-time onboard RUL estimation for electric vehicle battery packs.}
}

@article{ZHANG2025109815,
title = {A novel local enhanced channel self-attention based on Transformer for industrial remaining useful life prediction},
journal = {Engineering Applications of Artificial Intelligence},
volume = {141},
pages = {109815},
year = {2025},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2024.109815},
url = {https://www.sciencedirect.com/science/article/pii/S0952197624019742},
author = {Zhizheng Zhang and Wen Song and Qiong Wu and Wenxu Sun and Qiqiang Li and Lei Jia},
keywords = {Remaining useful life prediction, Predictive maintenance, Artificial intelligence, Deep learning, Transformer, Self-attention},
abstract = {Remaining useful life (RUL) prediction is a foundational technique for predictive maintenance (PdM) and is critical to ensuring the reliability and safety of complex industrial machines. Recently, while advanced deep learning architectures like recurrent neural network (RNN), convolutional neural network (CNN) and self-attention (SA) have been widely used for RUL prediction, existing methods still face difficulties in simultaneously processing global long-term dependencies and local contextual information of sequence units as well as the spatial correlations of industrial multi-sensors. In this article, we propose local enhanced channel self-attention based on Transformer (LECformer), a novel deep RUL prediction method to overcome these issues. LECformer can more effectively capture the long-term dependencies by Transformer architecture compared with RNN/CNN-based methods. Moreover, LECformer proposes a novel local enhanced channel self-attention (LECSA) mechanism to replace the traditional SA of vanilla Transformer, which can adaptively extract both long-term dependencies and local contextual information, while dynamically weighting the importance of different channels to improve predictive performance. Two widely used turbofan engine datasets and a bearing dataset are applied to validate the effectiveness of the proposed method. Experimental results show that the LECformer significantly outperforms the state-of-the-art RUL prediction methods.}
}

@article{GARCIANIETO2015219,
title = {Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability},
journal = {Reliability Engineering \& System Safety},
volume = {138},
pages = {219-231},
year = {2015},
issn = {0951-8320},
doi = {https://doi.org/10.1016/j.ress.2015.02.001},
url = {https://www.sciencedirect.com/science/article/pii/S0951832015000460},
author = {P.J. {García Nieto} and E. García-Gonzalo and F. {Sánchez Lasheras} and F.J. {de Cos Juez}},
keywords = {Support vector machines (SVMs), Particle swarm optimization (PSO), Aircraft engine, Remaining useful life and reliability prediction},
abstract = {The present paper describes a hybrid PSO–SVM-based model for the prediction of the remaining useful life of aircraft engines. The proposed hybrid model combines support vector machines (SVMs), which have been successfully adopted for regression problems, with the particle swarm optimization (PSO) technique. This optimization technique involves kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. However, its use in reliability applications has not been yet widely explored. Bearing this in mind, remaining useful life values have been predicted here by using the hybrid PSO–SVM-based model from the remaining measured parameters (input variables) for aircraft engines with success. A coefficient of determination equal to 0.9034 was obtained when this hybrid PSO–RBF–SVM-based model was applied to experimental data. The agreement of this model with experimental data confirmed its good performance. One of the main advantages of this predictive model is that it does not require information about the previous operation states of the engine. Finally, the main conclusions of this study are exposed.}
}

@article{CHEN2019123,
title = {Hidden Markov model with auto-correlated observations for remaining useful life prediction and optimal maintenance policy},
journal = {Reliability Engineering \& System Safety},
volume = {184},
pages = {123-136},
year = {2019},
note = {Impact of Prognostics and Health Management in Systems Reliability and Maintenance Planning},
issn = {0951-8320},
doi = {https://doi.org/10.1016/j.ress.2017.09.002},
url = {https://www.sciencedirect.com/science/article/pii/S0951832017301710},
author = {Zhen Chen and Yaping Li and Tangbin Xia and Ershun Pan},
keywords = {Remaining useful life prediction, Preventive maintenance, Hidden Markov models, Auto-correlated observations},
abstract = {In this paper, a hidden Markov model with auto-correlated observations (HMM-AO) is developed to handle the degradation modeling of manufacturing systems. Unlike the standard hidden Markov models (HMMs), the current observation in the HMM-AO model not only depends on the corresponding hidden system state, but also on the previous observations. A novel algorithm using the expectation maximum is presented to estimate the unknown parameters. Furthermore, missing data and noise that accumulate over time are also considered by modifying the proposed model. Then two remaining useful life prediction methods based on the HMM-AO model are developed. Predictive values of more accuracy can be obtained, since the autocorrelation of observations has been considered and the temporal evolution of degradation processes has been described properly. A case study is illustrated to highlight the advantages of HMM-AO and demonstrate the accuracy and efficiency of the prediction methods. Furthermore, an improved maintenance policy is developed based on the results of remaining useful life prediction. Finally, a comparison with a conventional condition-based maintenance policy is provided to prove the performance of this proposed policy.}
}

@article{ELSHEIKH2019148,
title = {Bidirectional handshaking LSTM for remaining useful life prediction},
journal = {Neurocomputing},
volume = {323},
pages = {148-156},
year = {2019},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2018.09.076},
url = {https://www.sciencedirect.com/science/article/pii/S0925231218311573},
author = {Ahmed Elsheikh and Soumaya Yacout and Mohamed-Salah Ouali},
keywords = {Remaining useful life prediction, Bidirectional handshaking, Long Short-Term Memory, Asymmetric objective function, Target generation},
abstract = {Unpredictable failures and unscheduled maintenance of physical systems increases production resources, produces more harmful waste for the environment, and increases system life cycle costs. Efficient remaining useful life (RUL) estimation can alleviate such an issue. The RUL is predicted by making use of the data collected from several types of sensors that continuously record different indicators about a working asset, such as vibration intensity or exerted pressure. This type of continuous monitoring data is sequential in time, as it is collected at a certain rate from the sensors during the asset's work. Long Short-Term Memory (LSTM) neural network models have been demonstrated to be efficient throughout the literature when dealing with sequential data because of their ability to retain a lot of information over time about previous states of the system. This paper proposes using a new LSTM architecture for predicting the RUL when given short sequences of monitored observations with random initial wear. By using LSTM, this paper proposes a new objective function that is suitable for the RUL estimation problem, as well as a new target generation approach for training LSTM networks, which requires making lesser assumptions about the actual degradation of the system.}
}

@article{FU2025125995,
title = {PSTFormer: A novel parallel spatial-temporal transformer for remaining useful life prediction of aeroengine},
journal = {Expert Systems with Applications},
volume = {265},
pages = {125995},
year = {2025},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2024.125995},
url = {https://www.sciencedirect.com/science/article/pii/S0957417424028628},
author = {Song Fu and Yiming Jia and Lin Lin and Shiwei Suo and Feng Guo and Sihao Zhang and Yikun Liu},
keywords = {Deep learning, Remaining useful life (RUL) prediction, Multidimensional time series (MTS), Spatiotemporal features, Feature fusion},
abstract = {One of the significant tasks in aeroengine remaining useful life (RUL) prediction is to address both temporal dependencies and spatial dependencies in multivariate time series (MTS) monitoring data. However, it is difficult for traditional transformer-based methods simultaneously extract both temporal and spatial dependencies due to their mutual interference, limiting the further improvement of prediction performance. To address these issues, this paper proposes a novel Parallel Spatial-Temporal Transformer (PSTFormer) for aeroengine RUL prediction with multi-sensor monitoring data. First, a novel parallel spatial–temporal attention mechanism (PSTAM) is designed, which consists of a temporal attention module (TAM) and a spatial attention module (SAM), to simultaneously capture temporal and spatial dependencies from MTS data. TAM employs multiscale convolution to learn the temporal dependencies at different time scales, while SAM adopts a self-attention mechanism to learn the spatial dependencies among different sensor parameter. Parallel connection between TAM and SAM can effectively avoid the mutual interference between temporal and spatial dependencies, improving the modeling ability of complex spatiotemporal relationships. Second, a task-guided spatiotemporal feature fusion (TG-STFF) module is designed, which adaptively fuses temporal and spatial features according to downstream task. Specifically, based on the RUL prediction characteristic, TG-STFF converts spatial features into attention weights and fuses them with temporal features to extract more representative degradation features. Finally, the effectiveness of PSTFormer is validated by a series of experimental comparisons on the public C-MAPSS dataset. Compared with SOTA methods, PSTFormer exhibits more outstanding prediction performance, and it can effectively address the aforementioned challenges in RUL prediction tasks. Therfore, the development of PSTFormer provides an innovative and effective method for aeroengine RUL prediction, significantly enhancing the efficiency and safety of aeroengine maintenance and operation.}
}

% 相关理论 - 退化指标构建
@ARTICLE{5710193,
  author={Malhi, Arnaz and Yan, Ruqiang and Gao, Robert X.},
  journal={IEEE Transactions on Instrumentation and Measurement}, 
  title={Prognosis of Defect Propagation Based on Recurrent Neural Networks}, 
  year={2011},
  volume={60},
  number={3},
  pages={703-711},
  keywords={Training;Neurons;Recurrent neural networks;Predictive models;Accuracy;Artificial neural networks;Continuous wavelet transforms;Competitive learning;continuous wavelet transform (CWT);long-term prediction},
  doi={10.1109/TIM.2010.2078296}}

@article{10.1115/1.1569940,
    author = {Antoni, J.  and Randall, R. B. },
    title = {A Stochastic Model for Simulation and Diagnostics of Rolling Element Bearings With Localized Faults },
    journal = {Journal of Vibration and Acoustics},
    volume = {125},
    number = {3},
    pages = {282-289},
    year = {2003},
    month = {06},
    abstract = {This paper addresses the stochastic modeling of the vibration signal produced by localized faults in rolling element bearings and its use for diagnostic purposes. The aim is essentially to provide a better understanding of the recognized “envelope analysis” technique as classically used in the diagnostics of rolling element bearings, and incidentally give theoretical proofs for the specific features of envelope spectra as obtained from experimental data. The proposed model may also prove useful for simulation purposes. First, the excitation force generated by a defect is modeled as a random point process and its spectral signature is derived analytically. Then its transmission through the bearing is investigated in detail in order to find the spectral characteristics of the resulting vibration signal. The analysis finally gives sound justification for “squared” envelope analysis and the type of spectral indicators that should be used with it.},
    issn = {1048-9002},
    doi = {10.1115/1.1569940},
    url = {https://doi.org/10.1115/1.1569940},
    eprint = {https://asmedigitalcollection.asme.org/vibrationacoustics/article-pdf/125/3/282/5828481/282\_1.pdf},
}

@INPROCEEDINGS{6299547,
  author={Wang, Tianyi},
  booktitle={2012 IEEE Conference on Prognostics and Health Management}, 
  title={Bearing life prediction based on vibration signals: A case study and lessons learned}, 
  year={2012},
  volume={},
  number={},
  pages={1-7},
  keywords={Feature extraction;Vibrations;Time frequency analysis;Fault detection;Predictive models;Indexes;Frequency modulation;bearing;envelop analysis;prognostics;remaining useful life;principal component analysis;vibration},
  doi={10.1109/ICPHM.2012.6299547}}

@INPROCEEDINGS{8819414,
  author={Huang, Dengshan and Wang, Meinan and Zhao, Shuai and Wen, Pengfei and Chen, Shaowei and Dou, Zhi},
  booktitle={2019 IEEE International Conference on Prognostics and Health Management (ICPHM)}, 
  title={An Improved Particle Filter Method for Accurate Remaining Useful Life Prediction}, 
  year={2019},
  volume={},
  number={},
  pages={1-8},
  keywords={Degradation;Mathematical model;Particle filters;Predictive models;Atmospheric measurements;Particle measurements;Market research;prognostics;degeneration trajectory;particle filter;measurement equation},
  doi={10.1109/ICPHM.2019.8819414}}

@ARTICLE{8612943,
  author={Kim, Minhee and Song, Changyue and Liu, Kaibo},
  journal={IEEE Transactions on Automation Science and Engineering}, 
  title={A Generic Health Index Approach for Multisensor Degradation Modeling and Sensor Selection}, 
  year={2019},
  volume={16},
  number={3},
  pages={1426-1437},
  keywords={Degradation;Data integration;Data models;Atmospheric modeling;Feature extraction;Indexes;Data fusion;degradation modeling;health index (HI);multisensor;predictive data analysis;prognostics},
  doi={10.1109/TASE.2018.2890608}}

@article{赵光权2018基于深度学习的轴承健康因子无监督构建方法,
  title={基于深度学习的轴承健康因子无监督构建方法},
  author={赵光权 and 刘小勇 and 姜泽东 and 胡聪},
  journal={仪器仪表学报},
  volume={39},
  number={6},
  pages={7},
  year={2018},
}

% 相关理论技术 - 退化过程模型
@article{WANG2021107504,
title = {Remaining Useful Life Prediction and Optimal Maintenance Time Determination for a Single Unit Using Isotonic Regression and Gamma Process Model},
journal = {Reliability Engineering \& System Safety},
volume = {210},
pages = {107504},
year = {2021},
issn = {0951-8320},
doi = {https://doi.org/10.1016/j.ress.2021.107504},
url = {https://www.sciencedirect.com/science/article/pii/S0951832021000673},
author = {Han Wang and Haitao Liao and Xiaobing Ma and Rui Bao},
keywords = {Cost constraint, Gamma process, Isotonic regression, Optimal maintenance time, Remaining useful life},
abstract = {Degradation-based remaining useful life (RUL) prediction plays an important role in ensuring the reliability and safety of rotating machinery components. The accuracy of traditional models and methods is usually affected by the inevitable fluctuations in degradation signals. This paper proposes a dynamic RUL prediction and optimal maintenance time (OMT) determination approach using a Gamma process model. This approach can significantly reduce the effects of random fluctuations on the accuracy of RUL prediction, and facilitate the implementation of real-time condition-based maintenance. In particular, an isotonic regression based data preprocessing method, called pool-adjacent-violators algorithm, is first presented to smooth random fluctuations in degradation signals. Then, health stage identification is conducted by measuring the degradation gradient within a sliding window to characterize the degradation trend and identify the jump points. A Bayesian algorithm and a maximum likelihood estimation method are jointly utilized to update the model parameters and further predict the component's RUL. By considering both maintenance cost and failure risk of the component, an OMT determination method based on RUL prediction result is developed. A case study on rolling element bearings illustrates the superiority and effectiveness of the proposed approach in both RUL prediction and maintenance decision making.}
}

@article{SONG2022108200,
title = {A common random effect induced bivariate gamma degradation process with application to remaining useful life prediction},
journal = {Reliability Engineering \& System Safety},
volume = {219},
pages = {108200},
year = {2022},
issn = {0951-8320},
doi = {https://doi.org/10.1016/j.ress.2021.108200},
url = {https://www.sciencedirect.com/science/article/pii/S0951832021006797},
author = {Kai Song and Lirong Cui},
keywords = {Bivariate degradation data, Expectation maximization algorithm, Gamma process, Maximum likelihood estimation, Remaining useful life prediction},
abstract = {Due to the complex structures and the multi-functionality of modern products, there are usually two or more performance characteristics which can reflect a product’s degradation states. The degradation processes corresponding to these performance characteristics are dependent in general, which brings challenges to the degradation data analysis. In this paper, a gamma process based degradation model is developed for the bivariate dependent degradation data, where the dependency between the two degradation processes is captured by a common random effect naturally. The expectation maximization algorithm is employed to estimate the model parameters. Then, a real-time prediction method for a product’s remaining useful life is proposed using the Bayesian method. Finally, both the simulation study and the case study are provided for illustration, whose results demonstrate that the proposed model as well as the corresponding inference methods does work well.}
}

@article{LIU2022108084,
title = {Gibbs sampler for noisy Transformed Gamma process: Inference and remaining useful life estimation},
journal = {Reliability Engineering \& System Safety},
volume = {217},
pages = {108084},
year = {2022},
issn = {0951-8320},
doi = {https://doi.org/10.1016/j.ress.2021.108084},
url = {https://www.sciencedirect.com/science/article/pii/S0951832021005822},
author = {Xingheng Liu and José Matias and Johannes Jäschke and Jørn Vatn},
keywords = {Transformed Gamma process, Measurement error, Gibbs sampler, Expectation–Maximization, Remaining useful life},
abstract = {Stochastic processes are widely used to describe continuous degradation, among which the monotonically increasing degradation is most common. However, the observation is often perturbed with undesired noise due to sensor or measurement errors in practice. This paper focuses on predicting the degradation growth and estimating the system’s remaining useful life based on noisy observations. The deterioration is modeled by a Transformed Gamma process, accounting for both time- and state-dependent degradation increments. Measurement error is assumed to follow a normal distribution. We propose to use an improved Gibbs sampler to estimate the hidden degradation states. Combined with Expectation–Maximization, the Gibbs sampler can be used for model parameter estimation. The probability of false/failed alarm and distribution of remaining useful life are also derived. The proposed method is applied to choke valve erosion data collected from NTNU’s laboratory, and the influence of covariates on the degradation rate is discussed.}
}

@article{CHEN2025110975,
title = {Remaining useful life prediction using a hybrid transfer learning-based adaptive Wiener process model},
journal = {Reliability Engineering \& System Safety},
volume = {260},
pages = {110975},
year = {2025},
issn = {0951-8320},
doi = {https://doi.org/10.1016/j.ress.2025.110975},
url = {https://www.sciencedirect.com/science/article/pii/S0951832025001784},
author = {Xiaowu Chen and Zhen Liu and Kunping Wu and Hanmin Sheng and Yuhua Cheng},
keywords = {Remaining useful life, Brownian motion-based drift coefficient, Transfer learning-based LSTM model, Wiener process, Adaptive fitting},
abstract = {Because of the characteristics of uncertainty description and interpretability, Wiener process (WP) has found extensive application in the domain of forecasting remaining useful life (RUL). Nevertheless, most existing WP often require selecting the suitable deterioration function and drift coefficient types based on the deterioration characteristics of target sample, which greatly limits their universality and feasibility in practical engineering. In order to address this issue, a hybrid adaptive WP based on transfer learning is presented to dynamically model the deterioration process of products with different deterioration features. The Brownian motion-based drift coefficient is applied to improve the adaptive characteristics of WP on the time-variant deterioration rate. A transfer learning-based long short-term memory (LSTM) model is utilized to adaptively track the dynamic nonlinear characteristics. According to the notion of first arrival time, we have successfully derived the explicit formula for the probability density function, so that the uncertainty contained in predicted results can be directly characterized. By using two capacity datasets and one torque bar deterioration dataset exhibiting distinct deterioration features, comparative experiments with eight different existing models have proven the universality and superiority of our model in forecasting RUL.}
}

@article{WANG2025110908,
title = {Remaining useful life prediction method based on two-phase adaptive drift Wiener process},
journal = {Reliability Engineering \& System Safety},
volume = {258},
pages = {110908},
year = {2025},
issn = {0951-8320},
doi = {https://doi.org/10.1016/j.ress.2025.110908},
url = {https://www.sciencedirect.com/science/article/pii/S0951832025001115},
author = {Zhijian Wang and Pengwei Jiang and Zhongxin Chen and Yanfeng Li and Weibo Ren and Lei Dong and Wenhua Du and Junyuan Wang and Xiaohong Zhang and Hui Shi},
keywords = {Two-phase wiener process model, Adaptive drift coefficient, Remaining useful life, First passage time},
abstract = {The degradation process of components often shows as two-phase in reality, and the two-phase Wiener process has been widely used to model component degradation. However, previous studies have always assumed that the drift coefficient of each phase is constant, failing to capture the effects of external variations, which reduces the predictive performance of model. Thus, this paper establishes a two-phase adaptive drift Wiener process model to characterize the degradation of components. First, a phasing method is proposed that adaptively identifies the change point and uses fitting metrics to analyze determine if the point is anomalous data. Additionally, the adaptive drift method is innovatively introduced into the developed two-phase Wiener process model for updates. Then, the approximate analytical expression of the probability density function of the remaining useful life is derived and extended to the cases where uncertainty in the state at the change point and heterogeneity are considered. Finally, the feasibility of the proposed method is validated through numerical simulation and actual examples in the laboratory.}
}

@ARTICLE{10463036,
  author={Cui, Xuemiao and Lu, Jiping and Han, Yafeng},
  journal={IEEE Access}, 
  title={Remaining Useful Life Prediction for Two-Phase Hybrid Deteriorating Lithium-Ion Batteries Using Wiener Process}, 
  year={2024},
  volume={12},
  number={},
  pages={43575-43599},
  keywords={Degradation;Predictive models;Trajectory;Data models;Analytical models;Estimation;Mathematical models;Lithium-ion batteries;Predictive models;Wiener filters;Parameter estimation;Numerical models;Gaussian processes;Lithium-ion batteries;RUL prediction;two-phase degradation;unit-to-unit variability;Wiener process},
  doi={10.1109/ACCESS.2024.3374776}}

@article{ZHUANG2024877,
title = {Remaining useful life prediction for two-phase degradation model based on reparameterized inverse Gaussian process},
journal = {European Journal of Operational Research},
volume = {319},
number = {3},
pages = {877-890},
year = {2024},
issn = {0377-2217},
doi = {https://doi.org/10.1016/j.ejor.2024.06.032},
url = {https://www.sciencedirect.com/science/article/pii/S0377221724005137},
author = {Liangliang Zhuang and Ancha Xu and Yijun Wang and Yincai Tang},
keywords = {Reliability, Adaptive replacement, Maintenance, Inverse Gaussian process, Remaining useful life},
abstract = {Two-phase degradation is a prevalent degradation mechanism observed in modern systems, typically characterized by a change in the degradation rate or trend of a system’s performance at a specific time point. Ignoring this change in degradation models can lead to considerable biases in predicting the remaining useful life (RUL) of the system, and potentially leading to inappropriate condition-based maintenance decisions. To address this issue, we propose a novel two-phase degradation model based on a reparameterized inverse Gaussian process. The model considers variations in both change points and model parameters among different systems to account for subject-to-subject heterogeneity. The unknown parameters are estimated using both maximum likelihood and Bayesian approaches. Additionally, we propose an adaptive replacement policy based on the distribution of RUL. By sequentially obtaining new degradation data, we dynamically update the estimation of model parameters and of the RUL distribution, allowing for adaptive replacement policies. A simulation study is conducted to assess the performance of our methodologies. Finally, a Lithium-ion battery example is provided to validate the proposed model and adaptive replacement policy. Technical details and additional results of case study are available as online supplementary materials.}
}

@ARTICLE{10317807,
  author={Jiang, Peihua and Wang, Bingxing and Wang, Xiaofei and Tsai, Tzong-Ru},
  journal={IEEE Transactions on Reliability}, 
  title={Reliability Assessment and Remaining Useful Life Prediction Based on the Inverse Gaussian Step-Stress Accelerated Degradation Data}, 
  year={2024},
  volume={73},
  number={2},
  pages={967-977},
  keywords={Degradation;Reliability engineering;Testing;Predictive models;Estimation;Gaussian processes;Life estimation;Accelerated degradation test (ADT);confidence interval (CI);inverse Gaussian (IG) process;prediction interval (PI);remaining useful life (RUL)},
  doi={10.1109/TR.2023.3328369}}

@ARTICLE{8941060,
  author={Chen, Xudan and Sun, Xinli and Si, Xiaosheng and Li, Guodong},
  journal={IEEE Access}, 
  title={Remaining Useful Life Prediction Based on an Adaptive Inverse Gaussian Degradation Process With Measurement Errors}, 
  year={2020},
  volume={8},
  number={},
  pages={3498-3510},
  keywords={Adaptation models;Degradation;Measurement errors;Data models;Predictive models;Reliability;Prognostics and health management;Adaptive model;inverse Gaussian process;measurement errors;remaining useful life},
  doi={10.1109/ACCESS.2019.2961951}}

% 相关理论技术 - 时间序列分解
@incollection{Barman25,
author = {Pankaj Das and Samir Barman},
title = {Perspective Chapter: An Overview of Time Series Decomposition and Its Applications},
booktitle = {Applied and Theoretical Econometrics and Financial Crises},
publisher = {IntechOpen},
address = {Rijeka},
year = {2025},
editor = {Prof. Brian William Sloboda and Dr. Chee-Heong Quah},
chapter = {0},
doi = {10.5772/intechopen.1009268},
url = {https://doi.org/10.5772/intechopen.1009268}
}

@InProceedings{10.1007/978-981-97-2242-6_4,
author="Wu, Yu-Xiang
and Dai, Bi-Ru",
editor="Yang, De-Nian
and Xie, Xing
and Tseng, Vincent S.
and Pei, Jian
and Huang, Jen-Wei
and Lin, Jerry Chun-Wei",
title="STL-ConvTransformer: Series Decomposition and Convolution-Infused Transformer Architecture in Multivariate Time Series Anomaly Detection",
booktitle="Advances in Knowledge Discovery and Data Mining",
year="2024",
publisher="Springer Nature Singapore",
address="Singapore",
pages="41--52",
abstract="In rapidly evolving industrial IT systems, the integration of sensor networks has become the cornerstone of operational workflows. These networks diligently collect data in the form of time series, where the format intertwines closely with temporal dependencies, crucial for anomaly detection models. Hence, the extraction of information in the time domain is advantageous for anomaly detection. To address this, we adopt a method of time series decomposition to delve into seasonality, trend, and residual components. Additionally, we design a novel algorithm that combines Transformer architecture with convolutional layers, focusing on subtle local dependencies within time series data. Extensive validation on three different real-world datasets highlights the robustness of our approach, demonstrating its proficiency in anomaly detection in time series materials. This underscores the advantage of combining convolutional strategies with Transformer architecture in capturing complex patterns and anomalies.",
isbn="978-981-97-2242-6"
}

@INPROCEEDINGS{10290126,
  author={Ouyang, Zuokun and Jabloun, Meryem and Ravier, Philippe},
  booktitle={2023 31st European Signal Processing Conference (EUSIPCO)}, 
  title={STLformer: Exploit STL Decomposition and Rank Correlation for Time Series Forecasting}, 
  year={2023},
  volume={},
  number={},
  pages={1405-1409},
  keywords={Correlation;Perturbation methods;Time series analysis;Europe;Predictive models;Signal processing;Transformers;Time Series;Forecasting;Transformer;Rank Correlation;Nonlinear Dependencies;STL Decomposition},
  doi={10.23919/EUSIPCO58844.2023.10290126}}

@article{LINDEMANN2021650,
title = {A survey on long short-term memory networks for time series prediction},
journal = {Procedia CIRP},
volume = {99},
pages = {650-655},
year = {2021},
note = {14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 15-17 July 2020},
issn = {2212-8271},
doi = {https://doi.org/10.1016/j.procir.2021.03.088},
url = {https://www.sciencedirect.com/science/article/pii/S2212827121003796},
author = {Benjamin Lindemann and Timo Müller and Hannes Vietz and Nasser Jazdi and Michael Weyrich},
keywords = {Recurrent Neural Networks, Long short-term memory, Autoencoder, Sequence-to-Sequence Networks, Time Series Prediction},
abstract = {Recurrent neural networks and exceedingly Long short-term memory (LSTM) have been investigated intensively in recent years due to their ability to model and predict nonlinear time-variant system dynamics. The present paper delivers a comprehensive overview of existing LSTM cell derivatives and network architectures for time series prediction. A categorization in LSTM with optimized cell state representations and LSTM with interacting cell states is proposed. The investigated approaches are evaluated against defined requirements being relevant for an accurate time series prediction. These include short-term and long-term memory behavior, the ability for multimodal and multi-step ahead predictions and the according error propagation. Sequence-to-sequence networks with partially conditioning outperform the other approaches, such as bidirectional or associative networks, and are best suited to fulfill the requirements.}
}

@article{LIN2022111,
title = {A survey of transformers},
journal = {AI Open},
volume = {3},
pages = {111-132},
year = {2022},
issn = {2666-6510},
doi = {https://doi.org/10.1016/j.aiopen.2022.10.001},
url = {https://www.sciencedirect.com/science/article/pii/S2666651022000146},
author = {Tianyang Lin and Yuxin Wang and Xiangyang Liu and Xipeng Qiu},
keywords = {Transformer, Self-attention, Pre-trained models, Deep learning},
abstract = {Transformers have achieved great success in many artificial intelligence fields, such as natural language processing, computer vision, and audio processing. Therefore, it is natural to attract lots of interest from academic and industry researchers. Up to the present, a great variety of Transformer variants (a.k.a. X-formers) have been proposed, however, a systematic and comprehensive literature review on these Transformer variants is still missing. In this survey, we provide a comprehensive review of various X-formers. We first briefly introduce the vanilla Transformer and then propose a new taxonomy of X-formers. Next, we introduce the various X-formers from three perspectives: architectural modification, pre-training, and applications. Finally, we outline some potential directions for future research.}
}

% 总体框架设计 - 退化指标构建
@article{李兴文2004交流接触器动态过程及触头弹跳的数值分析与实验研究,
  title={交流接触器动态过程及触头弹跳的数值分析与实验研究},
  author={李兴文 and 陈德桂 and 孙志强 and 李志鹏 and 纽春萍},
  journal={中国电机工程学报},
  volume={24},
  number={9},
  pages={5},
  year={2004},
}

@article{GARDNER2006637,
title = {Exponential smoothing: The state of the art—Part II},
journal = {International Journal of Forecasting},
volume = {22},
number = {4},
pages = {637-666},
year = {2006},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2006.03.005},
url = {https://www.sciencedirect.com/science/article/pii/S0169207006000392},
author = {Everette S. Gardner},
keywords = {Time series—ARIMA, exponential smoothing, state-space models, identification, stability, invertibility, model selection, Comparative methods—evaluation, Intermittent demand, Inventory control, Prediction intervals, Regression—discount weighted, kernel},
abstract = {In Gardner [Gardner, E. S., Jr. (1985). Exponential smoothing: The state of the art. Journal of Forecasting 4, 1–28], I reviewed the research in exponential smoothing since the original work by Brown and Holt. This paper brings the state of the art up to date. The most important theoretical advance is the invention of a complete statistical rationale for exponential smoothing based on a new class of state-space models with a single source of error. The most important practical advance is the development of a robust method for smoothing damped multiplicative trends. We also have a new adaptive method for simple smoothing, the first such method to demonstrate credible improved forecast accuracy over fixed-parameter smoothing. Longstanding confusion in the literature about whether and how to renormalize seasonal indices in the Holt–Winters methods has finally been resolved. There has been significant work in forecasting for inventory control, including the development of new predictive distributions for total lead-time demand and several improved versions of Croston's method for forecasting intermittent time series. Regrettably, there has been little progress in the identification and selection of exponential smoothing methods. The research in this area is best described as inconclusive, and it is still difficult to beat the application of a damped trend to every time series.}
}

@article{Jolliffe2016Principal,
  author = {Jolliffe, Ian T. and Cadima, Jorge},
  title = {Principal component analysis: a review and recent developments},
  journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
  volume = {374},
  number = {20150202},
  year = {2016},
  doi = {10.1098/rsta.2015.0202},
  url = {http://doi.org/10.1098/rsta.2015.0202}
}

% 退化轨迹分解
@article{S2024123542,
title = {State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges},
journal = {Applied Energy},
volume = {369},
pages = {123542},
year = {2024},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2024.123542},
url = {https://www.sciencedirect.com/science/article/pii/S0306261924009255},
author = {Vignesh S and Hang Seng Che and Jeyraj Selvaraj and Kok Soon Tey and Jia Woon Lee and Hussain Shareef and Rachid Errouissi},
keywords = {State of Health estimation, Second life, Non-destructive, Equipment, Challenges, Efforts},
abstract = {Lithium-ion Batteries (LiB) have a wide range of applications in daily life. However, as they get used over time, battery degradation becomes inevitable, which can lead to a drop in performance and a reduction in the battery’s cycle life. The State of Health (SoH) is widely regarded as the health indicator for the battery pack. In Electric Vehicle (EV) applications, the EV user defines the lower limit of SoH when they experience that the battery no longer supports the EV; at that point, the battery is said to be translated from first life to second life. The SoH estimations of Second Life Batteries (SLB) have plenty of uncertainties, such as the availability of battery’s previous history, non-uniform degradation in the EV application, variations in chemistry, and charging protocols defined by vehicle manufacturers, making the SoH estimation of SLB a challenging task. This paper discusses the equipment, timelines, computational complexity, health indicators, and list of parameters that need to be considered for the SoH estimation of SLB. The SoH estimation methods are classified into direct and indirect techniques. Direct assessment techniques involve cyclic ageing experiments followed by dismantling the battery for microscopic studies performed by previous researchers that were explained. Indirect assessment techniques include physical and chemical based approach, electrical, and Artificial Intelligence (AI)-based methods that estimate SoH indirectly through incremental, differential approaches and other parameters such as Integrated Voltage (IV) and Probability Density Function (PDF). Health indicator identifications play a vital role in indirect assessment methods to gain critical insights regarding battery degradation. The challenges involved in SoH estimation are categorized into equipment requirements, parameters, SoH accuracy and efforts required to compute SoH, which are discussed. Of all the SoH estimation methods, comparison of such methods in First Life Batteries (FLB) and SLB perspectives are discussed. To estimate the SoH of SLB, this paper explains all aspects, such as computational methods, filtering data, data sampling frequency, and the need for a specific algorithm to post-process the battery test data. Equipment availability and timelines are interrelated with the cost incurred in the SoH estimation of SLB. The efficacy and practicality of SoH estimation methods that are proposed for SLB is discussed. Overall, this paper provides necessary insights into the parameters required for SoH estimation and the computational and experimental methods that can be considered for estimating the SoH of SLB while some of the methods are applicable to FLB as well.}
}

% 事件扰动项通道
@article{CHU1995147,
title = {Time series segmentation: A sliding window approach},
journal = {Information Sciences},
volume = {85},
number = {1},
pages = {147-173},
year = {1995},
issn = {0020-0255},
doi = {https://doi.org/10.1016/0020-0255(95)00021-G},
url = {https://www.sciencedirect.com/science/article/pii/002002559500021G},
author = {Chia-Shang James Chu},
abstract = {The aim of this paper is to present two on-line, sliding window segmentation algorithms. Detection nonstationarity is based on parameter fluctuations and change point localization of the Akaike information criterion. Asymptotic properties of the proposed algorithms are analyzed. Specifically, the limiting distributions are derived and the asymptotic threshold values are tabulated for future reference. Finite sample simulations are performed to illustrate the usefulness of these algorithms.}
}

@article{10.1007/s11634-018-0335-0,
author = {Hallac, David and Nystrup, Peter and Boyd, Stephen},
title = {Greedy Gaussian segmentation of multivariate time series},
year = {2019},
issue_date = {Sep 2019},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
volume = {13},
number = {3},
issn = {1862-5347},
url = {https://doi.org/10.1007/s11634-018-0335-0},
doi = {10.1007/s11634-018-0335-0},
abstract = {We consider the problem of breaking a multivariate (vector) time series into segments over which the data is well explained as independent samples from a Gaussian distribution. We formulate this as a covariance-regularized maximum likelihood problem, which can be reduced to a combinatorial optimization problem of searching over the possible breakpoints, or segment boundaries. This problem can be solved using dynamic programming, with complexity that grows with the square of the time series length. We propose a heuristic method that approximately solves the problem in linear time with respect to this length, and always yields a locally optimal choice, in the sense that no change of any one breakpoint improves the objective. Our method, which we call greedy Gaussian segmentation (GGS), easily scales to problems with vectors of dimension over 1000 and time series of arbitrary length. We discuss methods that can be used to validate such a model using data, and also to automatically choose appropriate values of the two hyperparameters in the method. Finally, we illustrate our GGS approach on financial time series and Wikipedia text data.},
journal = {Adv. Data Anal. Classif.},
month = sep,
pages = {727–751},
numpages = {25},
keywords = {Time series analysis, Change-point detection, Financial regimes, Text segmentation, Covariance regularization, Greedy algorithms}
}

@mastersthesis{
陈瑞瑞,
author={陈瑞瑞},
title={基于小波变换的电弧故障检测技术研究},
school={杭州电子科技大学},
year={2012},
type={硕士论文},
month={},
}

@article{WANG2022108704,
title = {Building degradation index with variable selection for multivariate sensory data},
journal = {Reliability Engineering \& System Safety},
volume = {227},
pages = {108704},
year = {2022},
issn = {0951-8320},
doi = {https://doi.org/10.1016/j.ress.2022.108704},
url = {https://www.sciencedirect.com/science/article/pii/S0951832022003295},
author = {Yueyao Wang and I-Chen Lee and Yili Hong and Xinwei Deng},
keywords = {Adaptive LASSO, General path model, Prognostics, Sensor selection, Splines, System health monitoring},
abstract = {The modeling and analysis of degradation data have been an active research area in reliability engineering for reliability assessment and system health management. As the sensor technology advances, multivariate sensory data are commonly collected for the underlying degradation process. However, most existing research on degradation modeling requires a univariate degradation index to be provided. Thus, constructing a degradation index for multivariate sensory data is a fundamental step in degradation modeling. In this paper, we propose a novel degradation index building method for multivariate sensory data with censoring. Based on an additive nonlinear model with variable selection, the proposed method can handle censored data, and can automatically select the informative sensor signals to be used in the degradation index. The penalized likelihood method with adaptive group penalty is developed for parameter estimation. We demonstrate that the proposed method outperforms existing methods via both simulation studies and analyses of the NASA jet engine sensor data.}
}

@INPROCEEDINGS{8819414,
  author={Huang, Dengshan and Wang, Meinan and Zhao, Shuai and Wen, Pengfei and Chen, Shaowei and Dou, Zhi},
  booktitle={2019 IEEE International Conference on Prognostics and Health Management (ICPHM)}, 
  title={An Improved Particle Filter Method for Accurate Remaining Useful Life Prediction}, 
  year={2019},
  volume={},
  number={},
  pages={1-8},
  keywords={Degradation;Mathematical model;Particle filters;Predictive models;Atmospheric measurements;Particle measurements;Market research;prognostics;degeneration trajectory;particle filter;measurement equation},
  doi={10.1109/ICPHM.2019.8819414}}

@ARTICLE{8612943,
  author={Kim, Minhee and Song, Changyue and Liu, Kaibo},
  journal={IEEE Transactions on Automation Science and Engineering}, 
  title={A Generic Health Index Approach for Multisensor Degradation Modeling and Sensor Selection}, 
  year={2019},
  volume={16},
  number={3},
  pages={1426-1437},
  keywords={Degradation;Data integration;Data models;Atmospheric modeling;Feature extraction;Indexes;Data fusion;degradation modeling;health index (HI);multisensor;predictive data analysis;prognostics},
  doi={10.1109/TASE.2018.2890608}}

@INPROCEEDINGS{4711414,
  author={Saxena, Abhinav and Goebel, Kai and Simon, Don and Eklund, Neil},
  booktitle={2008 International Conference on Prognostics and Health Management}, 
  title={Damage propagation modeling for aircraft engine run-to-failure simulation}, 
  year={2008},
  volume={},
  number={},
  pages={1-9},
  keywords={Aircraft propulsion;Prognostics and health management;Engines;NASA;Life estimation;Turbines;Response surface methodology;Thermal sensors;Time measurement;Space vehicles;Damage modeling;Prognostics;C-MAPSS;Turbofan engines;Performance Evaluation},
  doi={10.1109/PHM.2008.4711414}}

@article{LI2020106113,
title = {Remaining useful life prediction using multi-scale deep convolutional neural network},
journal = {Applied Soft Computing},
volume = {89},
pages = {106113},
year = {2020},
issn = {1568-4946},
doi = {https://doi.org/10.1016/j.asoc.2020.106113},
url = {https://www.sciencedirect.com/science/article/pii/S1568494620300533},
author = {Han Li and Wei Zhao and Yuxi Zhang and Enrico Zio},
keywords = {Remaining useful life, Convolutional neural network, Multi-scale, Deep learning},
abstract = {Accurate and reliable remaining useful life (RUL) assessment result provides decision-makers valuable information to take suitable maintenance strategy to maximize the equipment usage and avoid costly failure. The conventional RUL prediction methods include model-based and data-driven. However, with the rapid development of modern industries, the physical model is becoming less capable of describing sophisticated systems, and the traditional data-driven methods have limited ability to learn sophisticated features. To overcome these problems, a multi-scale deep convolutional neural network (MS-DCNN) which have powerful feature extraction capability due to its multi-scale structure is proposed in this paper. This network constructs a direct relationship between Condition Monitoring (CM) data and ground-RUL without using any prior information. The MS-DCNN has three multi-scale blocks (MS-BLOCKs), where three different sizes of convolution operations are put on each block in parallel. This structure improves the network’s ability to learn complex features by extracting features of different scales. The developed algorithm includes three stages: data pre-processing, model training, and RUL prediction. After the min–max normalization pre-processing, the data is sent to the MS-DCNN network for parameter training directly, and the associated RUL value can be estimated base on the learned representations. Regularization helps to improve prediction accuracy and alleviate the overfitting problem. We evaluate the method on the available modular aero-propulsion system simulation data (C-MAPSS dataset) from NASA. The results show that the proposed method achieves good prognostics performance compared with other network architectures and state-of-the-art methods. RUL prediction result is obtained precisely without increasing the calculation burden.}
}

@article{Mo2021,
  author = {Mo, Yu and Wu, Qianhui and Li, Xiu and Huang, Biqing},
  title = {Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit},
  journal = {Journal of Intelligent Manufacturing},
  volume = {32},
  pages = {1997--2006},
  year = {2021},
  doi = {10.1007/s10845-021-01750-x},
  url = {https://doi.org/10.1007/s10845-021-01750-x},
  abstract = {Remaining Useful Life (RUL) estimation is a fundamental task in the prognostic and health management (PHM) of industrial equipment and systems. To this end, we propose a novel approach for RUL estimation in this paper, based on deep neural architecture due to its great success in sequence learning. Specifically, we take the Transformer encoder as the backbone of our model to capture short- and long-term dependencies in a time sequence. Compared with convolutional neural network based methods, there is no limitation from the kernel size for a complete receptive field of all time steps. While compared with recurrent neural network based methods, we develop our model based on dot-product self-attention, enabling it to fully exploit parallel computation. Moreover, we further propose a gated convolutional unit to facilitate the model’s ability of incorporating local contexts at each time step, for the attention mechanism used in the Transformer encoder makes the output high-level features insensitive to local contexts. We conduct experiments on the C-MAPSS datasets and show that, the performance of our model is superior or comparable to those of other existing methods. We also carry out ablation studies and demonstrate the necessity and effectiveness of each component used in the proposed model.},
  ISSN = {1572-8145}
}

@ARTICLE{9905490,
  author={Gong, Ran and Li, Jinxiao and Wang, Chenlin},
  journal={IEEE Sensors Journal}, 
  title={Remaining Useful Life Prediction Based on Multisensor Fusion and Attention TCN-BiGRU Model}, 
  year={2022},
  volume={22},
  number={21},
  pages={21101-21110},
  keywords={Convolution;Predictive models;Sensor phenomena and characterization;Data models;Convolutional neural networks;Time series analysis;Task analysis;Attention mechanism;bidirectional gate recurrent unit;multisensor signal fusion;remaining useful life (RUL);temporal convolutional neural network},
  doi={10.1109/JSEN.2022.3208753}}


@article{ZHANG2023109096,
title = {An integrated multi-head dual sparse self-attention network for remaining useful life prediction},
journal = {Reliability Engineering \& System Safety},
volume = {233},
pages = {109096},
year = {2023},
issn = {0951-8320},
doi = {https://doi.org/10.1016/j.ress.2023.109096},
url = {https://www.sciencedirect.com/science/article/pii/S095183202300011X},
author = {Jiusi Zhang and Xiang Li and Jilun Tian and Hao Luo and Shen Yin},
keywords = {Multi-head self-attention, Remaining useful life, Transformer, Sparse strategy, Prediction},
abstract = {Committed to accident prevention, prediction of remaining useful life (RUL) plays a crucial role in prognostics health management technology. Conventional convolutional neural network and long-short-term memory network have notable limitations in the size of convolution in processing temporal data and the associations between non-adjacent data when predicting the RUL, respectively. Although the proposal of the Transformer provides an opportunity to solve the shortcomings mentioned above, Transformer still has some limitations. Precisely, the Transformer model awaits in-depth research focusing on vital local regions and decreasing computational complexity. In this sense, this paper proposes a novel integrated multi-head dual sparse self-attention network (IMDSSN) based on a modified Transformer to predict the RUL. From two sparse perspectives, the proposed IMDSSN includes a multi-head ProbSparse self-attention network (MPSN) and a multi-head LogSparse self-attention network (MLSN). Specifically, MPSN is designed to filter out the primary function of the dot product operation, thereby improving computational efficiency. Furthermore, considering the data inside the whole time window, a comprehensive logarithmic-based sparse strategy in MLSN is proposed to reduce the amount of computation. An aircraft turbofan engine dataset is used to verify the proposed IMDSSN, which demonstrates that the IMDSSN is better than some conventional approaches.}
}

@article{LIANG2023107722,
title = {Remaining useful life prediction via a deep adaptive transformer framework enhanced by graph attention network},
journal = {International Journal of Fatigue},
volume = {174},
pages = {107722},
year = {2023},
issn = {0142-1123},
doi = {https://doi.org/10.1016/j.ijfatigue.2023.107722},
url = {https://www.sciencedirect.com/science/article/pii/S0142112323002232},
author = {Pengfei Liang and Ying Li and Bin Wang and Xiaoming Yuan and Lijie Zhang},
keywords = {Remaining useful life, Adaptive transformer, Graph attention network, Multi-sensor data, Information fusion},
abstract = {Accurate monitoring of mechanical device conditions requires a large number of sensors working together. There are potential connections between sensors throughout the degradation monitoring process of mechanical devices. Conventional deep learning (DL) models suffer from the following shortcomings when dealing with this type of multi-sensor degraded data. To begin with, most existing methods based on DL mainly use CNN as the feature extractor, focusing too much on temporal correlations and ignoring spatial correlations of multiple sensors. Then, the most popular remaining useful life (RUL) model is based on recurrent neural network, which oftentimes suffer from the issue of gradient exploding and vanishing. Therefore, a bran-new end-to-end framework based on a deep adaptative transformer enhanced by graph attention network, named GAT-DAT, is proposed to tackle these weaknesses. First, the graph data is constructed by the correlation of sensors. Next, GAT submodules fuse node features to extract spatial correlation. Finally, the DAT submodule is used to efficiently abstract the temporal features of the data through a self-attention mechanism and adaptively implements RUL prediction for mechanical equipment. Two case studies are employed to attest the efficacy of our proposed GAT-DAT model and the analysis of the experimental data illustrates that the GAT-DAT framework outperforms the existing state-of-the-art methods.}
}

@article{Yin2024,
  author = {Yin, Yuyan and Tian, Jie and Liu, Xinfeng},
  title = {Remaining useful life prediction based on parallel multi-scale feature fusion network},
  journal = {Journal of Intelligent Manufacturing},
  year = {2024},
  month = {May},
  day = {08},
  doi = {10.1007/s10845-024-02399-y},
  url = {https://doi.org/10.1007/s10845-024-02399-y},
  issn = {1572-8145},
  abstract = {In the domain of Predictive Health Management (PHM), the prediction of Remaining Useful Life (RUL) is pivotal for averting machinery malfunctions and curtailing maintenance expenditures. Currently, most RUL prediction methods overlook the correlation between local and global information, which may lead to the loss of important features and, consequently, a subsequent decline in predictive precision. To address these limitations, this study presents a groundbreaking deep learning framework termed the Parallel Multi-Scale Feature Fusion Network (PM2FN). This approach leverages the advantages of different network structures by constructing two distinct feature extractors to capture both global and local information, thereby providing a more comprehensive feature set for RUL prediction. Experimental results on two publicly available datasets and a real-world dataset demonstrate the superiority and effectiveness of our method, offering a promising solution for industrial RUL prediction.}
}

@article{ZHANG2024107241,
title = {An attention-based temporal convolutional network method for predicting remaining useful life of aero-engine},
journal = {Engineering Applications of Artificial Intelligence},
volume = {127},
pages = {107241},
year = {2024},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2023.107241},
url = {https://www.sciencedirect.com/science/article/pii/S0952197623014252},
author = {Qiang Zhang and Qiong Liu and Qin Ye},
keywords = {Remaining useful life prediction, Temporal convolutional network, Attention mechanism},
abstract = {Researches on Remaining Useful Life (RUL) prediction of aero-engine could help to make maintenance plans, improve operation reliabilities and reduce maintenance costs. While deep learning methods have been widely used in RUL prediction research, most deep learning-based RUL prediction methods tend to treat input features as equally important. Contributions of different channels and time steps from input features are not considered simultaneously, which will inevitably affect efficiencies and accuracies of RUL prediction. Therefore, a novel deep learning-based RUL prediction method named attention-based temporal convolutional network (ATCN) is proposed in this article. First, an improved self-attention mechanism is used to weight contributions of different time steps from input features. Input features of time steps closely related to RUL are enhanced by the improved self-attention mechanism, which could improve efficiencies of feature extraction in a network. Then, a temporal convolutional network is constructed to capture long-term dependent information and extract feature representations from weighted features of the improved self-attention mechanism. Next, a squeeze-and-excitation mechanism is adopted to weight contributions of different channels from feature representations, which could help to improve prediction accuracies of the network. Finally, a fully connected layer is constructed to fuse weighted features to output RUL values. A commercial modular aero-propulsion system simulation (C-MAPSS) dataset from NASA is applied to verify effects of the proposed method. Performances of the proposed method are compared with those based on different neural network architectures, such as CNN, RNN, LSTM, DCNN, TCN, BiGRU-TSAM, AGCNN and channel attention plus Transformer. Results show that the proposed method could yield results with higher accuracy for RUL prediction of aero-engine than other methods.}
}

@ARTICLE{10843190,
  author={Kazmi, Syed Hussain Ali and Hassan, Rosilah and Qamar, Faizan and Nisar, Kashif and Al-Betar, Mohammed Azmi},
  journal={IEEE Access}, 
  title={Federated Conditional Variational Auto Encoders for Cyber Threat Intelligence: Tackling Non-IID Data in SDN Environments}, 
  year={2025},
  volume={13},
  number={},
  pages={26273-26288},
  keywords={Federated learning;Training;Cyber threat intelligence;Data models;Security;Intrusion detection;Data privacy;Accuracy;6G mobile communication;Software defined networking;Deep learning;non-IID data;6G;VAE;CTI},
  doi={10.1109/ACCESS.2025.3529894}}

@data{q7b4-fs93-25,
doi = {10.21227/q7b4-fs93},
url = {https://dx.doi.org/10.21227/q7b4-fs93},
author = {Wu, mingyue},
publisher = {IEEE Dataport},
title = {CMPASS Dataset},
year = {2025} }

@article{Attention_is_all_you_need,
  author       = {Ashish Vaswani and
                  Noam Shazeer and
                  Niki Parmar and
                  Jakob Uszkoreit and
                  Llion Jones and
                  Aidan N. Gomez and
                  Lukasz Kaiser and
                  Illia Polosukhin},
  title        = {Attention Is All You Need},
  journal      = {CoRR},
  volume       = {abs/1706.03762},
  year         = {2017},
  url          = {http://arxiv.org/abs/1706.03762},
  eprinttype    = {arXiv},
  eprint       = {1706.03762},
  timestamp    = {Sat, 23 Jan 2021 01:20:40 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/VaswaniSPUJGKP17.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@incollection{Barman25,
author = {Pankaj Das and Samir Barman},
title = {Perspective Chapter: An Overview of Time Series Decomposition and Its Applications},
booktitle = {Applied and Theoretical Econometrics and Financial Crises},
publisher = {IntechOpen},
address = {Rijeka},
year = {2025},
editor = {Prof. Brian William Sloboda and Dr. Chee-Heong Quah},
chapter = {0},
doi = {10.5772/intechopen.1009268},
url = {https://doi.org/10.5772/intechopen.1009268}
}

@article{EHMKE2012338,
title = {Floating car based travel times for city logistics},
journal = {Transportation Research Part C: Emerging Technologies},
volume = {21},
number = {1},
pages = {338-352},
year = {2012},
issn = {0968-090X},
doi = {https://doi.org/10.1016/j.trc.2011.11.004},
url = {https://www.sciencedirect.com/science/article/pii/S0968090X11001562},
author = {Jan Fabian Ehmke and Stephan Meisel and Dirk Christian Mattfeld},
keywords = {City logistics, Floating Car Data, Time-dependent travel times, Routing, Data Mining},
abstract = {City logistics routing requires time-dependent travel times for each network link. We rely on the concept of Floating Car Data (FCD) to develop and provide such travel times. Different levels of aggregation in the determination of time-dependent travel times from a database of historical FCD are presented and evaluated with regard to routing quality. Furthermore, a Data Mining approach is introduced, allowing for a substantial reduction of the volume of input data required for city logistics routing. The different approaches are investigated and evaluated by a huge amount of FCD collected for the urban area of Stuttgart, Germany. The results show that the Data Mining approach enables efficient provision of time-dependent travel times without a significant loss of routing quality for city logistics applications.}
}

@book{2003Segmenting,
  title={Segmenting Time Series: A Survey and Novel Approach},
  author={ Keogh, Eamonn  and  Chu, Selina  and  Hart, David  and  Pazzani, Michael },
  publisher={Segmenting Time Series: A Survey and Novel Approach},
  year={2003},
}

@ARTICLE{1672634,
  author={Pavlidis, T. and Horowitz, S.L.},
  journal={IEEE Transactions on Computers}, 
  title={Segmentation of Plane Curves}, 
  year={1974},
  volume={C-23},
  number={8},
  pages={860-870},
  keywords={Boundary segmentation, data compaction, feature extracton, pattern recognition, piecewise functional approximation, polygonal contours, waveform segmentation.},
  doi={10.1109/T-C.1974.224041}}

@article{Lin2019,
  author = {林意 and 朱志静},
  title = {基于趋势的时间序列分段线性化算法},
  journal = {重庆大学学报},
  year = {2019},
  volume = {42},
  number = {3},
  pages = {92-98},
  doi = {}
}

@INPROCEEDINGS{836610,
  author={Park, S. and Lee, D. and Chu, W.W.},
  booktitle={Proceedings 1999 Workshop on Knowledge and Data Engineering Exchange (KDEX'99) (Cat. No.PR00453)}, 
  title={Fast retrieval of similar subsequences in long sequence databases}, 
  year={1999},
  volume={},
  number={},
  pages={60-67},
  keywords={Information retrieval;Databases;Indexing;Time measurement;Euclidean distance;Computer science;Ear;Velocity measurement;Data mining;Sampling methods},
  doi={10.1109/KDEX.1999.836610}}

@article{LIU2025113147,
title = {TVC Former: A transformer-based long-term multivariate time series forecasting method using time-variable coupling correlation graph},
journal = {Knowledge-Based Systems},
volume = {314},
pages = {113147},
year = {2025},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2025.113147},
url = {https://www.sciencedirect.com/science/article/pii/S0950705125001947},
author = {Zhenyu Liu and Yuan Feng and Hui Liu and Ruining Tang and Bo Yang and Donghao Zhang and Weiqiang Jia and Jianrong Tan},
keywords = {Time series forecasting, Transformer, Graph neural network, Data mining},
abstract = {Long-term multivariate time series forecasting is crucial in various domains that require the effective modeling of intervariable dependencies in series data. However, existing methods tend to capture these dependencies directly across the entire series, thus neglecting the local dynamic characteristics of the intervariable correlation patterns caused by locality differences and the dynamic variability of the series. To address this, we propose TVC Former, a forecasting model that uses a time-variable coupling correlation graph (TVC graph). The TVC graph treats local window-level subsequences as nodes and explicitly models local intervariable dependence. Its structure is dynamically and adaptively generated to effectively represent task-specific valuable intervariable local correlation patterns while eliminating irrelevant ones. Specifically, a sparsified graph structure is initialized based on the correlation between the input historical series and statistical similarity between the subsequences. It is then optimized using a pattern capture-fusion sparsification unit with learnable parameters. In addition, we propose a time-variable joint-encoding framework with a transformer encoder as the backbone. By introducing local head markers and a graph neural network, the framework effectively captures the intervariable dependencies using the TVC graph. Experiments on seven real-world datasets demonstrate the superiority of TVC Former in long-term forecasting tasks.}
}

@ARTICLE{10901945,
  author={Abdulmaksoud, Ahmed and Ahmed, Ryan},
  journal={IEEE Access}, 
  title={Transformer-Based Sensor Fusion for Autonomous Vehicles: A Comprehensive Review}, 
  year={2025},
  volume={13},
  number={},
  pages={41822-41838},
  keywords={Sensor fusion;Transformers;Laser radar;Autonomous vehicles;Cameras;Computational modeling;Reviews;Three-dimensional displays;Object detection;Location awareness;Autonomous driving;artificial intelligence (AI);computer vision;deep learning;machine learning;sensor fusion;transformers},
  doi={10.1109/ACCESS.2025.3545032}}

@article{DBLP:journals/access/AhsanHRA25,
  author       = {Muhammad Ahsan and
                  Muhammad Waqar Hassan and
                  Jos{\'{e}} Rodr{\'{\i}}guez and
                  Mohamed Abdelrahem},
  title        = {Enhanced Fault Diagnosis in Rotating Machinery Using a Hybrid CWT-LeNet-5-LSTM
                  Model: Performance Across Various Load Conditions},
  journal      = {{IEEE} Access},
  volume       = {13},
  pages        = {1026--1045},
  year         = {2025},
  url          = {https://doi.org/10.1109/ACCESS.2024.3522948},
  doi          = {10.1109/ACCESS.2024.3522948},
  timestamp    = {Tue, 14 Jan 2025 21:21:25 +0100},
  biburl       = {https://dblp.org/rec/journals/access/AhsanHRA25.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@ARTICLE{10689393,
  author={Khaled Alarfaj, Fawaz and Shahzadi, Shabnam},
  journal={IEEE Access}, 
  title={Enhancing Fraud Detection in Banking With Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud Prevention}, 
  year={2025},
  volume={13},
  number={},
  pages={20633-20646},
  keywords={Fraud;Deep learning;Banking;Credit cards;Business intelligence;Real-time systems;Graph neural networks;Fraud;Detection algorithms;Deep learning;credit card;fraud detection;graph neural network;autoencoders},
  doi={10.1109/ACCESS.2024.3466288}}

@mastersthesis{田琨2015,
author={田琨},
title={ 基于主元分析的交流接触器的关键特征参数分析 },
school={河北工业大学},
year={2015},
type={硕士论文},
month={},
}

@INPROCEEDINGS{phm08LSTM,
  author={Zheng, Shuai and Ristovski, Kosta and Farahat, Ahmed and Gupta, Chetan},
  booktitle={2017 IEEE International Conference on Prognostics and Health Management (ICPHM)}, 
  title={Long Short-Term Memory Network for Remaining Useful Life estimation}, 
  year={2017},
  volume={},
  number={},
  pages={88-95},
  keywords={Hidden Markov models;Estimation;Data models;Logic gates;Recurrent neural networks;Prognostics and health management},
  doi={10.1109/ICPHM.2017.7998311}}

@InProceedings{phm08CNN,
author="Sateesh Babu, Giduthuri
and Zhao, Peilin
and Li, Xiao-Li",
editor="Navathe, Shamkant B.
and Wu, Weili
and Shekhar, Shashi
and Du, Xiaoyong
and Wang, X. Sean
and Xiong, Hui",
title="Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life",
booktitle="Database Systems for Advanced Applications",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="214--228",
abstract="Prognostics technique aims to accurately estimate the Remaining Useful Life (RUL) of a subsystem or a component using sensor data, which has many real world applications. However, many of the existing algorithms are based on linear models, which cannot capture the complex relationship between the sensor data and RUL. Although Multilayer Perceptron (MLP) has been applied to predict RUL, it cannot learn salient features automatically, because of its network structure. A novel deep Convolutional Neural Network (CNN) based regression approach for estimating the RUL is proposed in this paper. Although CNN has been applied on tasks such as computer vision, natural language processing, speech recognition etc., this is the first attempt to adopt CNN for RUL estimation in prognostics. Different from the existing CNN structure for computer vision, the convolution and pooling filters in our approach are applied along the temporal dimension over the multi-channel sensor data to incorporate automated feature learning from raw sensor signals in a systematic way. Through the deep architecture, the learned features are the higher-level abstract representation of low-level raw sensor signals. Furthermore, feature learning and RUL estimation are mutually enhanced by the supervised feedback. We compared with several state-of-the-art algorithms on two publicly available data sets to evaluate the effectiveness of this proposed approach. The encouraging results demonstrate that our proposed deep convolutional neural network based regression approach for RUL estimation is not only more efficient but also more accurate.",
isbn="978-3-319-32025-0"
}

@article{phm08VAE_RNN,
title = {Variational encoding approach for interpretable assessment of remaining useful life estimation},
journal = {Reliability Engineering \& System Safety},
volume = {222},
pages = {108353},
year = {2022},
issn = {0951-8320},
doi = {https://doi.org/10.1016/j.ress.2022.108353},
url = {https://www.sciencedirect.com/science/article/pii/S0951832022000321},
author = {Nahuel Costa and Luciano Sánchez},
keywords = {Remaining useful life, Prognostics and health management, Interpretability, Variational inference, Recurrent neural networks},
abstract = {A new method for evaluating aircraft engine monitoring data is proposed. Commonly, prognostics and health management systems use knowledge of the degradation processes of certain engine components together with professional expert opinion to predict the Remaining Useful Life (RUL). New data-driven approaches have emerged to provide accurate diagnostics without relying on such costly processes. However, most of them lack an explanatory component to understand model learning and/or the nature of the data. To overcome this gap we propose a novel approach based on variational encoding. The model consists of a recurrent encoder and a regression model: the encoder learns to compress the input data to a latent space that serves as a basis to build a self-explanatory map that can visually evaluate the rate of deterioration of aircraft engines. Obtaining such a latent space is regularized by a new cost function guided by variational inference and a term that penalizes prediction errors. Consequently, not only an interpretable assessment is achieved but also a remarkable prognostic accuracy, outperforming most of the state-of-the-art approaches on the popular simulation dataset C-MAPSS from NASA. In addition, we demonstrate the application of our method in a real-world scenario with data from actual Turbofan engines.}
}

@article{phm08Transformer,
author = {Wang, Hai-Kun and Cheng, Yi and Song, Ke},
title = {Remaining Useful Life Estimation of Aircraft Engines Using a Joint Deep Learning Model Based on TCNN and Transformer},
journal = {Computational Intelligence and Neuroscience},
volume = {2021},
number = {1},
pages = {5185938},
doi = {https://doi.org/10.1155/2021/5185938},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1155/2021/5185938},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1155/2021/5185938},
abstract = {The remaining useful life estimation is a key technology in prognostics and health management (PHM) systems for a new generation of aircraft engines. With the increase in massive monitoring data, it brings new opportunities to improve the prediction from the perspective of deep learning. Therefore, we propose a novel joint deep learning architecture that is composed of two main parts: the transformer encoder, which uses scaled dot-product attention to extract dependencies across distances in time series, and the temporal convolution neural network (TCNN), which is constructed to fix the insensitivity of the self-attention mechanism to local features. Both parts are jointly trained within a regression module, which implies that the proposed approach differs from traditional ensemble learning models. It is applied on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset from the Prognostics Center of Excellence at NASA Ames, and satisfactory results are obtained, especially under complex working conditions.},
year = {2021}
}

@INPROCEEDINGS{phm08BiLSTM,
  author={Wang, Jiujian and Wen, Guilin and Yang, Shaopu and Liu, Yongqiang},
  booktitle={2018 Prognostics and System Health Management Conference (PHM-Chongqing)}, 
  title={Remaining Useful Life Estimation in Prognostics Using Deep Bidirectional LSTM Neural Network}, 
  year={2018},
  volume={},
  number={},
  pages={1037-1042},
  keywords={Data models;Estimation;Integrated circuit modeling;Logic gates;Hidden Markov models;Computer architecture;Prognostics and health management;prognostics and health management;remaining useful life;bidirectional LSTM;deep learning},
  doi={10.1109/PHM-Chongqing.2018.00184}}

@article{phm08DAG_network,
  title={A directed acyclic graph network combined with CNN and LSTM for remaining useful life prediction},
  author={Li, Jialin and Li, Xueyi and He, David},
  journal={IEEE Access},
  volume={7},
  pages={75464--75475},
  year={2019},
  publisher={IEEE}
}

@ARTICLE{phm08TCN,
  author={Gong, Ran and Li, Jinxiao and Wang, Chenlin},
  journal={IEEE Sensors Journal}, 
  title={Remaining Useful Life Prediction Based on Multisensor Fusion and Attention TCN-BiGRU Model}, 
  year={2022},
  volume={22},
  number={21},
  pages={21101-21110},
  keywords={Convolution;Predictive models;Sensor phenomena and characterization;Data models;Convolutional neural networks;Time series analysis;Task analysis;Attention mechanism;bidirectional gate recurrent unit;multisensor signal fusion;remaining useful life (RUL);temporal convolutional neural network},
  doi={10.1109/JSEN.2022.3208753}}

@article{phm08IMDSSN,
title = {An integrated multi-head dual sparse self-attention network for remaining useful life prediction},
journal = {Reliability Engineering \& System Safety},
volume = {233},
pages = {109096},
year = {2023},
issn = {0951-8320},
doi = {https://doi.org/10.1016/j.ress.2023.109096},
url = {https://www.sciencedirect.com/science/article/pii/S095183202300011X},
author = {Jiusi Zhang and Xiang Li and Jilun Tian and Hao Luo and Shen Yin},
keywords = {Multi-head self-attention, Remaining useful life, Transformer, Sparse strategy, Prediction},
abstract = {Committed to accident prevention, prediction of remaining useful life (RUL) plays a crucial role in prognostics health management technology. Conventional convolutional neural network and long-short-term memory network have notable limitations in the size of convolution in processing temporal data and the associations between non-adjacent data when predicting the RUL, respectively. Although the proposal of the Transformer provides an opportunity to solve the shortcomings mentioned above, Transformer still has some limitations. Precisely, the Transformer model awaits in-depth research focusing on vital local regions and decreasing computational complexity. In this sense, this paper proposes a novel integrated multi-head dual sparse self-attention network (IMDSSN) based on a modified Transformer to predict the RUL. From two sparse perspectives, the proposed IMDSSN includes a multi-head ProbSparse self-attention network (MPSN) and a multi-head LogSparse self-attention network (MLSN). Specifically, MPSN is designed to filter out the primary function of the dot product operation, thereby improving computational efficiency. Furthermore, considering the data inside the whole time window, a comprehensive logarithmic-based sparse strategy in MLSN is proposed to reduce the amount of computation. An aircraft turbofan engine dataset is used to verify the proposed IMDSSN, which demonstrates that the IMDSSN is better than some conventional approaches.}
}

@article{phm08KGHM,
title = {Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models},
journal = {Reliability Engineering \& System Safety},
volume = {229},
pages = {108869},
year = {2023},
issn = {0951-8320},
doi = {https://doi.org/10.1016/j.ress.2022.108869},
url = {https://www.sciencedirect.com/science/article/pii/S0951832022004860},
author = {Yuanfu Li and Yao Chen and Zhenchao Hu and Huisheng Zhang},
keywords = {Knowledge, Deep learning, CNN, LSTM, RUL prediction},
abstract = {The remaining useful life (RUL) prediction of a complex engineering system is extremely significant for ensuring system reliability. The conventional prediction of the RUL based on only extracted degradation features of sensor data is tedious for decreasing costs and providing a decision-making foundation. However, knowledge is available for improving RUL prediction accuracy. This paper proposes a novel RUL prediction approach that combines knowledge and deep learning models. The proposed approach represents the sensor relationships as flow charts to be transformed as embedding vectors for clustering. These clustering results are subsequently utilized to guide the sensor data arrangement and hybrid deep learning model construction. Compared to various deep learning models, the robustness and reliability of the proposed method are demonstrated on the NASA open dataset C-MAPSS. The results show that the proposed approach had improved prediction accuracy by 5.5% compared to the best prediction from the literature methods. Furthermore, the constructed deep learning model by utilizing knowledge can be interpretable. Most importantly, the prediction results reveal the feasibility and reliability of fusing knowledge and deep learning models. And the proposed approach is promising for widespread application to other prediction situations with data from numerous sensors.}
}

@article{phm08ATCN,
title = {An attention-based temporal convolutional network method for predicting remaining useful life of aero-engine},
journal = {Engineering Applications of Artificial Intelligence},
volume = {127},
pages = {107241},
year = {2024},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2023.107241},
url = {https://www.sciencedirect.com/science/article/pii/S0952197623014252},
author = {Qiang Zhang and Qiong Liu and Qin Ye},
keywords = {Remaining useful life prediction, Temporal convolutional network, Attention mechanism},
abstract = {Researches on Remaining Useful Life (RUL) prediction of aero-engine could help to make maintenance plans, improve operation reliabilities and reduce maintenance costs. While deep learning methods have been widely used in RUL prediction research, most deep learning-based RUL prediction methods tend to treat input features as equally important. Contributions of different channels and time steps from input features are not considered simultaneously, which will inevitably affect efficiencies and accuracies of RUL prediction. Therefore, a novel deep learning-based RUL prediction method named attention-based temporal convolutional network (ATCN) is proposed in this article. First, an improved self-attention mechanism is used to weight contributions of different time steps from input features. Input features of time steps closely related to RUL are enhanced by the improved self-attention mechanism, which could improve efficiencies of feature extraction in a network. Then, a temporal convolutional network is constructed to capture long-term dependent information and extract feature representations from weighted features of the improved self-attention mechanism. Next, a squeeze-and-excitation mechanism is adopted to weight contributions of different channels from feature representations, which could help to improve prediction accuracies of the network. Finally, a fully connected layer is constructed to fuse weighted features to output RUL values. A commercial modular aero-propulsion system simulation (C-MAPSS) dataset from NASA is applied to verify effects of the proposed method. Performances of the proposed method are compared with those based on different neural network architectures, such as CNN, RNN, LSTM, DCNN, TCN, BiGRU-TSAM, AGCNN and channel attention plus Transformer. Results show that the proposed method could yield results with higher accuracy for RUL prediction of aero-engine than other methods.}
}

@article{phm08PM2FN,
  title={Remaining useful life prediction based on parallel multi-scale feature fusion network},
  author={Yin, Yuyan and Tian, Jie and Liu, Xinfeng},
  journal={Journal of Intelligent Manufacturing},
  pages={1--17},
  year={2024},
  publisher={Springer}
}